Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914673219076096 |
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| author | Chang, Junwoo Ryu, Hyunwoo Kim, Jiwoo Yoo, Soochul Choi, Jongeun Seo, Joohwan Prakash, Nikhil Horowitz, Roberto |
| author_facet | Chang, Junwoo Ryu, Hyunwoo Kim, Jiwoo Yoo, Soochul Choi, Jongeun Seo, Joohwan Prakash, Nikhil Horowitz, Roberto |
| contents | Diffusion models have risen as a powerful tool in robotics due to their flexibility and multi-modality. While some of these methods effectively address complex problems, they often depend heavily on inference-time obstacle detection and require additional equipment. Addressing these challenges, we present a method that, during inference time, simultaneously generates only reachable goals and plans motions that avoid obstacles, all from a single visual input. Central to our approach is the novel use of a collision-avoiding diffusion kernel for training. Through evaluations against behavior-cloning and classical diffusion models, our framework has proven its robustness. It is particularly effective in multi-modal environments, navigating toward goals and avoiding unreachable ones blocked by obstacles, while ensuring collision avoidance. Project Website: https://sites.google.com/view/denoising-heat-inspired |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_12609 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning Chang, Junwoo Ryu, Hyunwoo Kim, Jiwoo Yoo, Soochul Choi, Jongeun Seo, Joohwan Prakash, Nikhil Horowitz, Roberto Robotics Artificial Intelligence Machine Learning Diffusion models have risen as a powerful tool in robotics due to their flexibility and multi-modality. While some of these methods effectively address complex problems, they often depend heavily on inference-time obstacle detection and require additional equipment. Addressing these challenges, we present a method that, during inference time, simultaneously generates only reachable goals and plans motions that avoid obstacles, all from a single visual input. Central to our approach is the novel use of a collision-avoiding diffusion kernel for training. Through evaluations against behavior-cloning and classical diffusion models, our framework has proven its robustness. It is particularly effective in multi-modal environments, navigating toward goals and avoiding unreachable ones blocked by obstacles, while ensuring collision avoidance. Project Website: https://sites.google.com/view/denoising-heat-inspired |
| title | Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2310.12609 |